Satellite Data Based Impact Assessment of Basin Characteristics for Brahmaputra River System of India
19 May 2011-pp 3792-3802
About: The article was published on 2011-05-19. It has received 1 citations till now. The article focuses on the topics: Impact assessment.
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TL;DR: This study suggest that this approach will be effective with an acceptable accuracy for crop map generation to classify the different crops and thereby calculate the actual crop water demand of commanded area of Kangsabati reservoir and thereby evaluate the performance of the canal system in near future.
Abstract: This paper presents the analysis of IRS LISS III satellite image based on NDVI (Normalized Difference Vegetation Index) for extracting the crop pattern of rabi season of Kangsabati commanded area located at Bankura district of West Bengal, India. NDVI was calculated through creation of FCC (False Colour Composite) followed by supervised classification by applying ground truth data obtained after physical survey on three districts, covered under Kangsabati reservoir commanded area. Final crop map is generated to extract different crop classes from which crop sown in rabi season is estimated. It has been found that potato crop is sown on much more area than rice. Finally, accuracy is checked, and 87% accuracy was achieved after successful identification of crop. This study suggest that this approach will be effective with an acceptable accuracy for crop map generation to classify the different crops and thereby calculate the actual crop water demand of commanded area of Kangsabati reservoir and thereby evaluate the performance of the canal system in near future.
1 citations
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TL;DR: In this article , the authors investigated the Muyuy anabranching planform change of the Upper Peruvian Amazon River due to the seasonal flow fluctuations and a severe flood in 2012.
Abstract: The Upper Amazon River forms an anabranching planform, which has been found to have significant changes in migration rate and river morphology. Previous studies have elaborated long-term evolution of the anabranching systems; however, research on the influence of the water level on temporal changes in anabranching is absent. According to the theory of river hydraulics, fluvial scour usually occurs when the shear force possessed by the high flow exceeds the resistance of the streambank. In contrast, deposition occurs when the tractive force of the low flow is insufficient to overcome the forces of gravity and friction. This study investigated the Muyuy anabranching planform change of the Upper Peruvian Amazon River due to the seasonal flow fluctuations and a severe flood in 2012. The Muyuy anabranching area is located 20 km upstream of Iquitos City, Peru. Landsat images from the wet and dry seasons in 2008, 2009, 2012, and 2013 were collected. The images were classified into three land cover classes (water, bare soil, vegetation and others) based on NDVI analysis. Quantitative analysis of the erosion/deposition shows that deposition is more noticeable than erosion in the Muyuy anabranching area. Considerable deposition can be found on the island of the anabranching system, and the streambank erosion occurred in the outer (concave) side of the main channel. This phenomenon of river erosion and deposition consistently occurred in 2008 and 2009 because of the periodical variation among the wet and dry seasons. However, prominent erosion was observed in 2012 and it was recognized to be caused by the severe flood. Furthermore, the extensive island was formed in 2013, which means substantial depositions accumulated in the recession of the 2012 flood.
References
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01 Dec 1995
TL;DR: Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications.
Abstract: For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information.
5,323 citations
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TL;DR: An evaluation of results indicates that various procedures of change detection produce different maps of change even in the same environment.
Abstract: A variety of procedures for change detection based on comparison of multitemporal digital remote sensing data have been developed. An evaluation of results indicates that various procedures of change detection produce different maps of change even in the same environment.
2,992 citations
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TL;DR: High levels of registration are needed for reliable monitoring of global change in the absence of any actual changes to the land surface, using spatially degraded Landsat MSS images.
Abstract: The impact of misregistration on the detection of changes in land cover has been evaluated using spatially degraded Landsat MSS images, focusing on simulated images of the normalized difference vegetation index (NDVI) Single-date images from seven areas were misregistered against themselves, and the statistical properties of the differences were analyzed In the absence of any actual changes to the land surface, the consequences of misregistration were very marked even for subpixel misregistrations Pairs of images from different time periods were then misregistered For four densely covered areas, an error equivalent to greater than 50% of the actual differences in the NDVI, as measured by the semivariance, was induced by a misregistration of only one pixel To achieve an error of only 10%, registration accuracies of 02 pixels or less are required For three more sparsely vegetated areas with semiarid climates, a registration accuracy of between 05 and 10 pixel was sufficient to achieve an error of 10% or less The results indicate that high levels of registration are needed for reliable monitoring of global change >
509 citations
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TL;DR: The results from false change analysis indicate a substantial degradation in the accuracy of remotely sensed change detection due to misregistration, and it is shown that a registration accuracy of less than one-fifth of a pixel is required to achieve a change detection error ofLess than 10%.
Abstract: Image misregistration has become one of the significant bottlenecks for improving the accuracy of multisource data analysis, such as data fusion and change detection. In this paper, the effects of misregistration on the accuracy of remotely sensed change detection were systematically investigated and quantitatively evaluated. This simulation research focused on two interconnected components. In the first component, the statistical properties of the multispectral difference images were evaluated using semivariograms when multitemporal images were progressively misregistered against themselves and each other to investigate the band, temporal, and spatial frequency sensitivities of change detection to image misregistration. In the second component, the ellipsoidal change detection technique, based on the Mahalanobis distance of multispectral difference images, was proposed and used to progressively detect the land cover transitions at each misregistration stage for each pair of multitemporal images. The impact of misregistration on change detection was then evaluated in terms of the accuracy of change detection using the output from the ellipsoidal change detector. The experimental results using Landsat Thematic Mapper (TM) imagery are presented. It is interesting to notice that, among the seven TM bands, band 4 (near-infrared channel) is the most sensitive to misregistration when change detection is concerned. The results from false change analysis indicate a substantial degradation in the accuracy of remotely sensed change detection due to misregistration. It is shown that a registration accuracy of less than one-fifth of a pixel is required to achieve a change detection error of less than 10%.
463 citations
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15 Dec 1999
TL;DR: A survey of multispectral methods for land cover change analysis can be found in this article, where the authors present an evaluation of the CoastWatch change detection protocol in South Carolina.
Abstract: Applications, Project Formulation, and Analytical Approach. Survey of Multispectral Methods for Land Cover Change Analysis. North American Landscape Characterization: Dataset Development/Data Fusion. Classification-Based Change Detection: Theory and Application to the NALC Dataset. An Evaluation of the CoastWatch Change Detection Protocol in South Carolina. Methods for Detecting Conifer Change with Thematic Mapper Data. Wildfire Detection with Meteorological Satellite Data. Detection of Fires and Power Outages using DMSP-OLS Data. Change Identification using Multitemporal Spatial Mixture Analysis. Seasonal Vegetation Patterns Derived from Advanced Visible/Infrared Imaging Spectrometer Data. Vegetation Change Detection using High Spectral Resolution Vegetation Indices. Radar Remote Sensing of Wetlands. Monitoring Trends in Wetlands Vegetation Using Landsat MSS Time Series. Radar Interferometry for Environmental Change Detection. Change Detection Accuracy Assessment.
429 citations
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